Search Results (135)

The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. This research aims to study the ML and DL systematic applications in optimizing the wastewater treatment processes from an industrial plant, such as the modeling of complex physical–chemical processes, real-time monitoring and prediction of critical wastewater quality indicators, chemical reactants consumption reduction, minimization of plant energy consumption, plant effluent quality prediction, development of data-driven type models as support in the decision-making process, etc. To perform a detailed analysis, 87 articles were included from an initial set of 324, using criteria such as wastewater combined with ML, DL, and artificial intelligence (AI), for articles from 2010 or newer. From the initial set of 324 scientific articles, 300 were identified using Litmaps, obtained from five important scientific databases, all focusing on addressing the specific problem proposed for investigation. Thus, this paper identifies gaps in the current research, discusses ML and DL algorithms in the context of optimizing wastewater treatment processes, and identifies future directions for optimizing these processes through data-driven methods. As opposed to traditional models, IA models (ML, DL, hybrid and ensemble models, digital twin, IoT, etc.) demonstrated significant advantages in wastewater quality indicator prediction and forecasting, in energy consumption forecasting, in temporal pattern recognition, and in optimal interpretability for normative compliance. Integrating advanced ML and DL technologies into the various processes involved in wastewater treatment improves the plant systems’ predictive capabilities and ensures a higher level of compliance with environmental standards. Full article

Ozonation and ozone-based advanced oxidation processes, including ozone/hydrogen peroxide and ozone/ultraviolet irradiation, have been extensively studied for their efficacy in treating wastewater across various industries. While sectors such as pulp and paper, textile, food and beverage, microelectronics, and municipal wastewater have successfully implemented ozone at full scale, others have yet to fully embrace these technologies’ effectiveness. This review article examines recent publications from the past two decades, exploring novel applications of ozone-based technologies in treating wastewater from diverse sectors, including food and beverage, agriculture, aquaculture, textile, pulp and paper, oil and gas, medical and pharmaceutical manufacturing, pesticides, cosmetics, cigarettes, latex, cork manufacturing, semiconductors, and electroplating industries. The review underscores ozone’s broad applicability in degrading recalcitrant synthetic and natural organics, thereby reducing toxicity and enhancing biodegradability in industrial effluents. Additionally, ozone-based treatments prove highly effective in disinfecting pathogenic microorganisms present in these effluents. Continued research and application of these ozonation and ozone-based advanced oxidation processes hold promise for addressing environmental challenges and advancing sustainable wastewater management practices globally. Full article

The recent Directive EU/2024/3019, a recast of the previous 1991 Directive 91/271/EEC concerning urban wastewater treatment, introduces new obligations concerning effluents requirements and overall energy management in urban wastewater systems. In addition to increased levels of treatment (including extended tertiary and quaternary pollutants removal), the Directive introduces the obligation for treatment facilities to become “energy neutral” at the national sectoral level, increasing reliance on energy optimization and recovery from internal processes and external renewable energy sources. In order to achieve this objective, an obligation to periodically conduct energy audits is introduced; however, while this practice is commonly carried out in residential and industrial buildings, guidelines for its implementation in treatment facilities are currently not precisely defined. The paper summarizes current issues on wastewater sector energy audits, discussing the current state-of-the-art and the expected requirements to conduct such audits. It then discusses the causes of possible facility inefficiencies and their possible solutions from both permanent and transient perspectives. Finally, it addresses the issue of energy neutrality requirement, and the role of renewable energy sources contribution, both natural and internal (process-related) to the sector’s energy efficiency. Full article

This study presents a biomonitoring study of surface waters in the Krynka River basin, encompassing three major regional reservoirs: Khanzhenkovskoe, Olkhovskoe, and Zuyevskoe. These water bodies face significant anthropogenic pressure from mining effluents, industrial discharges, and domestic wastewater. Key pollutants identified are surfactants (SAAs), sulfates, phenols, chlorides, and manganese, with chemical oxygen demand (COD) exceeding regulatory limits. The research was conducted in September 2024. Based on the Specific Combinatorial Water Pollution Index, surface waters in the studied objects can be characterized as slightly polluted. To assess the negative impact of the identified pollutants on hydrobionts, the species composition of phytoplankton of the studied water bodies was analyzed. In the Olkhovskoe Reservoir and Olkhovaya River, cyanobacterial blooms (Oscillatoria agardhii G.) were observed, altering biodiversity in the Krynka River and Zuyevskoe Reservoir. Phytoplankton genera Synedra, Amphiprora, and Navicula—established bioindicators of aquatic ecosystem health—were dominant in Khanzhenkovskoe Reservoir, signaling nutrient enrichment and organic pollution. Changes in the species composition and structure of phytoplankton in the Krynka River, its tributaries and reservoirs, indicate a change in the level of saprobic water bodies from β to α-mesosaprobic, which indicates both the general level of surface water pollution and the accumulation of pollutants along the course of the river. The paper presents the results of fluorimetric analysis of photosynthetic activity of natural phytoplankton cells and demonstrates the possibility of using fluorescence induction curves for regular monitoring measurements. Fluorescence parameters indicate a general deterioration of photosynthetic activity of natural phytoplankton. The growth of Oscillatoria agardhii in the waters of the Olkhovskoe Reservoir and of green microalgae in the Zuevskoe Reservoir led to an increase in the fluorescence quantum yield (F_v/F_m) and the total photosynthetic activity index (PI), which makes it possible to use these parameters as indicator parameters reflecting the intensity of “blooming” of various phytoplankton species. Full article

This paper establishes an explicable integrated machine learning model for predicting the discharge water quality in a circulating cooling water system of a power plant. The performance differences between three deep learning models, a Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), and a Convolutional Neural Network (CNN), and traditional machine learning models, namely eXtreme Gradient Boosting (XGboost) and Support Vector Machine (SVM), were evaluated and compared. The TCN model has high fitting accuracy and low error in predicting ammonia nitrogen, nitrate nitrogen, total nitrogen, chemical oxygen demand (COD), and total phosphorus in the effluent of a circulating cooling tower. Compared to other traditional machine learning models, the TCN has a larger R² (maximum 0.911) and lower Root Mean Square Error (RMSE, minimum 0.158) and Mean Absolute Error (MAE, minimum 0.118), indicating the TCN has better feature extraction and fitting performance. Although the TCN takes additional time, it is generally less than 1 s, enabling the real-time prediction of drainage water quality. The main water quality indices have the greatest causal inference relationship with those of makeup water, followed by the concentration ratio, indicating that concentrations of ammonia nitrogen, nitrate nitrogen, total nitrogen, and COD have a more decisive impact. Shapley Additive Explanations (SHAP) analysis further reveals that the concentration ratio has a weaker decisive impact on circulating cooling water drainage quality. The results of this study facilitate the optimization of industrial water resource management and offer a feasible technical pathway for water resource utilization in power plants. Full article

Unregulated irrigation with partially industrial effluents may lead to heavy metal contamination in crops and pose significant human health risks, especially in developing countries like India. Therefore, the present study aimed to quantify six heavy metals (Cd, Cr, Cu, Fe, Mn, and Zn) in soil and wheat irrigated with paper mill effluent, assess plant responses, and evaluate associated health risks for consumers. For this, a field study was conducted across ten sites (five effluent-irrigated, five borewell-irrigated as control), analyzing soil and wheat tissues for metal concentrations and calculating risk indices including bioaccumulation factor (Bf), translocation factor (Tf), Dietary Intake of Metals (DIM < 1), Health Risk Index (HRI < 1), and Target Hazard Quotient (THQ < 1). Results indicated high concentrations of Cd and Cr in effluent-irrigated soils and wheat tissues (root > stem > leaves > grains) compared to control sites, with some values exceeding permissible limits. Although the THQ values for heavy metals were below 1, indicating a low immediate health risk, concentrations of Cd and Cr in both soil and crop tissues exceeded acceptable safety standards. This study provides empirical evidence supporting the need for effluent treatment and policy interventions to mitigate agricultural contamination from the use of industrial effluents and protect public health. Full article

The environmental impacts of industrial processes have increased the demand for sustainable alternatives in wastewater treatment. Conventional chemical coagulants, though widely used, can generate toxic residues and pose environmental and health risks. Biocoagulants, derived from natural and renewable sources, offer a biodegradable and eco-friendly alternative. This review explores their potential to replace synthetic coagulants by analyzing their origins, mechanisms of action, and applications. A total of 15 studies published between 2020 and 2025 were analyzed, all focused on industrial wastewater. These studies demonstrated that biocoagulants can achieve similar, or the superior, removal of turbidity (>67%), solids (>83%), and heavy metals in effluents from food, textile, metallurgical, and paper industries. While raw materials are often inexpensive, processing costs may increase production expenses. However, life cycle assessments suggest long-term advantages due to reduced sludge and environmental impact. A textile industry case study showed a 25% sludge reduction and improved biodegradability using a plant-based biocoagulant compared to aluminum sulfate. Transforming this waste into inputs for wastewater treatment not only reduces negative impacts from disposal but also promotes integrated environmental management aligned with circular economy and cleaner production principles. The review concludes that biocoagulants constitute a viable and sustainable alternative for industrial wastewater treatment. Full article

To address the challenge of low computational efficiency in nonlinear Economic Model Predictive Control (EMPC) for large-scale systems such as wastewater treatment plants (WWTPs), this paper proposes a Trajectory Piecewise Linearization (TPWL)-based EMPC framework integrated with global maximum error control (GMEC) and Proper Orthogonal Decomposition (POD). The TPWL method constructs a reduced-order model framework, while GMEC iteratively refines the linearization point selection process. A two-stage strategy is employed: first, coarse selection of candidate linearization points along the original nonlinear model’s state trajectory based on Euclidean distance, followed by refinement to determine optimal points that minimize global approximation errors. Simulation results demonstrate that the proposed method reduces computational time by at least 65% under identical weather conditions while maintaining effluent quality and total cost indices within acceptable thresholds. Compared with conventional TPWL-POD approaches, this framework achieves higher model accuracy and superior EMPC control performance. These advancements underscore the method’s potential for real-time implementation in complex industrial systems, balancing computational efficiency with control precision. Additionally, the framework’s modular design enables integration with existing optimization techniques to further reduce computational complexity without compromising effluent quality compliance. Full article

The use of paper sludge as a waste stream from industrial facilities represents a significant environmental challenge due to its quantity and heterogeneous composition. The aim of the study was to evaluate the adsorption characteristics of paper sludge in neutral mine effluents and aquatic solutions of metal ions: Cu(II), Zn(II), Cd(II), and Mn(II). The main novelty of the research is a comparison of the adsorption process in synthetically prepared aquatic solutions and neutral mine drainage from field sampling. The adsorption process of the monitored metals was evaluated in terms of adsorption capacity, parameters of the Freundlich and Langmuir adsorption isotherm, and the separation factor. The adsorption capacity of paper sludge of all metals is significantly lower in neutral mine drainage (NMD) compared to adsorption in aquatic solution. The adsorption capacity of Zn(II) in aqueous solution reaches equilibrium over time, similarly to Cu(II), with values ranging from 0.2 to 1.6 mg/g. For Cd(II), a slight increasing trend in the adsorption capacity of paper sludge is observed at higher initial concentrations (3–5 mg/L) over a contact time of 90–120 min. In general, aqueous solutions of metal ions exhibited higher adsorption capacities compared to NMD, with the highest value recorded for Cu(II) at 4.742 mg/g. As the concentration values in the original solution increased, a decline in K_R (from 268% to 137% at a C0 range of 4–20 mg/L) was observed. In the mine drainage with the addition of Zn(II), K_R values were also lower compared to those in aquatic solutions. The reduction in K_R became more pronounced with increasing initial concentration, showing a decrease of 29.9% to 38.9% at C0 levels ranging from 2 to 10 mg/L. The separation factors for Cu(II), Zn(II), and Cd(II) were lower in NMD, indicating better metal separation from real mine waters. The results confirm the potential of paper sludge as a low-cost adsorbent for the treatment of heavy metal contaminated waters. Full article

The dyeing industry plays a substantial role in environmental pollution, primarily through the release of wastewater that contains a variety of chemicals into aquatic ecosystems. Synthetic dyes play a crucial role in numerous sectors, including textiles, tanning, food production and pharmaceuticals. However, the effluents generated by industries that utilize these dyes are regarded as detrimental to both the environment and human health. Additionally, wastewater may include a range of chemical additives utilized during the dyeing process, including fixing agents, surfactants and pH adjusters. Various techniques for dye remediation have been extensively studied. Nevertheless, effective and economically viable methods for dye removal have yet to be fully developed. This paper emphasizes and provides an overview of the recent literature concerning the application of the most commonly accessible biogenic materials in the context of dye removal by the adsorption process. Various biogenic adsorbents sourced from plants, algae, microorganisms and biopolymers contain bioactive compounds that interact with the functional groups of dyes, leading to their attachment to the sorbent. By mechanical, thermal and chemical modifications of these materials, their adsorption capabilities could be increased. Full article

Wastewater treatment plants (WWTPs) play a crucial role in mitigating microplastic (MP) release to the environment. In this paper, a WWTP of a textile manufacturing plant in Guangdong, China, was investigated to identify MP characteristics and the effectiveness of wastewater treatment within the plant. Laser Direct Infrared (LDIR) and Liquid Chromatography with Mass Spectrometry (LC-MS/MS) were applied to quantify both the number and the mass of the microplastics in the effluent of the textile manufacturing plant where most of the wastewater were from three printing and dyeing lines. The study further investigated the MP removal efficiency of each wastewater treatment process of the industry-owned WWTP and analysed the removal mechanism of each step, highlighting limitations in detecting and eliminating MPs. It is observed that (1) the results from LDIR and LC-MS/MS can be complementary to each other; (2) the MP concentration in the influent was 1730 n/L by number and 13.52 µg/L by mass; (3) the total removal efficiency of the WWTP were 99% by the number of MPs and 67.7% by the mass of MPs; (4) nine types of polymers have been identified in the influent, of which Polyamide (PA) was dominating; (5) hydrolysis acidification removed PA most; (6) aerobic tank, sand filter, and biological aerated filter (BAF) showed low removal efficiency; (7) coagulation and sedimentation tank had the highest removal efficiency to PET than any other processes. Full article

In order to address the issues of system fluctuation and low efficiency brought on by various disturbance factors in wastewater treatment plants (WWTPs), this study builds a robust multi-objective optimization model and determines the ideal parameters for enhancing the robustness and stability of energy consumption (EC) and effluent quality (EQ). Firstly, a novel robust index is proposed, merging the signal-to-noise ratio and standard deviation., comprehensively considering data volatility and anti-interference capability to guide the establishment of a robust optimization model. Secondly, considering the energy consumption errors and water quality data measurement caused by noise factors, which increases the difficulty and time complexity of the solution. Therefore, this paper uses a multi-objective mantis search algorithm (MOMSA) based on a robust subgroup, and an information synergy strategy is used to solve the problem. The strategy first divides the population into strong and weak robust subgroups according to their robustness. Then, the co-evolutionary strategy and the information enhancement strategy will be used to guide the evolutionary direction of the individual from the whole and the local perspectives, thereby comprehensively improving the convergence speed of the algorithm. Based on the test function, this shows that the convergence, diversity, and comprehensive performance of the improved algorithm are better than other algorithms. Experimental results based on the BSM1 platform show that the improved algorithm is effective in finding robust solutions with strong stability despite changes in external factors (e.g., precipitation). According to the experimental results based on real industrial problems, the new robustness metrics can effectively evaluate the robustness of the optimization model, and compared with other algorithms, the algorithm proposed in this paper yields solutions of more reliable quality and faster convergence performance, which can successfully solve the robust optimization problem in wastewater treatment plants. Full article

In this paper, a lab-scale reactor designed to simulate the operations of the North Water Saline Wastewater Treatment Plant (SWWTP) located in Delfzijl, The Netherlands, was constructed and assessed. Unlike conventional municipal wastewater treatment facilities, this industrial plant deals with wastewater containing stubborn chemicals that are difficult to break down, along with a high ratio of chemical oxygen demand (COD) to nitrogen and elevated sodium chloride levels. Furthermore, its treatment process diverges from standard industrial setups by employing an aerobic process preceding the anaerobic phase. The proposed lab-scale reactors were proven stable and effective in mimicking the conditions of the studied industrial SWWTP, particularly in the presence of abundant glycerol, a factor not explored in similar lab-scale models. Throughout the experiment, the removal of COD (specifically glycerol) and nitrogen were monitored, alongside changes in the microbial community within both reactors. The data enabled us to examine the proliferation of microbial populations within the sludge. The results indicated the complete removal of glycerol and ammonia from the system, with some residual nitrate detected in the effluent. The soluble COD decreased in the first reactor (R1) to approximately 50% of the influent and reduced further to less than 100 mg/L in the second reactor (R2), while nitrogen was majorly removed in the R1. By the experiment’s conclusion, Actinomycetales was identified as the dominant order in the anaerobic reactor (sometimes even exceeding 70% of the population), which is known for its utilization of glycerol as a carbon source and its tolerance to high salt concentrations in the influent. Conversely, the aerobic reactor was predominantly inhabited by the order Flavobacteriales, which correlates with ammonia concentration. Full article

Microplastic fibre (MPF) pollution is a pressing concern that demands urgent attention. These tiny synthetic textile fibres can be found in various ecosystems, including water and air, and pose significant environmental risks. Despite their size (less than 5 mm), they can harm aquatic and terrestrial organisms and human health. Studies have demonstrated that these imperceptible pollutants can contaminate marine environments, thereby putting marine life at risk through ingestion and entanglement. Additionally, microplastic fibres can absorb toxins from the surrounding water, heightening their danger when consumed by aquatic organisms. Traces of MPFs have been identified in human food chains and organs. To effectively combat MPF pollution, it is crucial to understand how these fibres enter ecosystems and their sources. Primary sources include domestic laundry, where synthetic textile fibres are released into wastewater during washing. Other significant sources include industrial effluents, breakdown of plastic materials, and atmospheric deposition. Additionally, MPFs can be directly released into the environment by improperly disposing of consumer products containing these fibres, such as non-woven hygienic products. A comprehensive approach is necessary to address this pressing issue, including understanding the sources, pathways, and potential risks of MPFs. Immediate action is required to manage contamination and mitigate MPF pollution. This review paper provides a systematic literature analysis to help stakeholders prioritise efforts towards reducing MPFs. The key knowledge gaps identified include a lack of information regarding non-standardised test methodology and reporting units, and a lack of information on manufacturing processes and products, to increase understanding of life cycle impacts and real hotspots. Stakeholders urgently need collaborative efforts to address the systematic changes required to tackle this issue and address the proposed opportunities, including targeted government interventions and viable strategies for the industry sector to lead action. Full article

Increasing the number of resistant bacteria resistant to treatment is one of the leading causes of death worldwide. These bacteria are created in wounds and injuries and can be transferred through hospital equipment. Various attempts have been made to treat these bacteria in recent years, such as using different drugs and new sterilization methods. However, some bacteria resist drugs, and other traditional methods cannot destroy them. In the meantime, various studies have shown that cold atmospheric plasma can kill these bacteria through different mechanisms, making cold plasma a promising tool to deactivate bacteria. This new technology can be effectively used in the food industry because it has the potential to inactivate microorganisms such as spores and microbial toxins and increase the wettability and printability of polymers to pack fresh and dried food. It can also increase the shelf life of food without leaving any residue or chemical effluent. This paper investigates cold plasma’s potential, advantages, and disadvantages in the food industry and sterilization. Full article